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Modeling the Severity of Right-Turn Crossing Conflicts at Unsignalized T-Intersections Using a Random-Parameter Binary Logit Model
https://orcid.org/0000-0003-4475-2573
https://orcid.org/0000-0002-0636-1355
In India, unsignalized T-intersections are more complex for the right-turn movement of vehicular traffic, which leads to serious crashes. The existing surrogate safety measures are not effective in computing the severity of right-turn crossing conflicts (RTCCs). In this context, the present study focused on a new indicator called critical speed (), which is calculated by adding the speed of the vehicle going through an intersection on a major road with the time taken by the immediate approaching vehicle (TTIA) to reach the conflict point of the vehicle leaving the intersection. RTCCs were identified using critical speeds from video data collected at five unsignalized T-intersections in three cities in India. A fixed-parameter binary logistic model (FPBLM) and a random-parameter binary logistic model (RPBLM) were developed to explore the contributing factors of severe RTCCs. The RPBLM results suggest that a higher proportion of two-wheelers and cars in conflicting vehicles significantly increases the severity of RTCCs. The study findings also indicated that traffic characteristics and driver behavioral characteristics need to be controlled in order to decrease the severity of RTCCs. The findings of this study help traffic engineers and safety experts to take suitable traffic management measures to reduce RTCCs at unsignalized T-intersections.
Modeling the Severity of Right-Turn Crossing Conflicts at Unsignalized T-Intersections Using a Random-Parameter Binary Logit Model
https://orcid.org/0000-0003-4475-2573
https://orcid.org/0000-0002-0636-1355
In India, unsignalized T-intersections are more complex for the right-turn movement of vehicular traffic, which leads to serious crashes. The existing surrogate safety measures are not effective in computing the severity of right-turn crossing conflicts (RTCCs). In this context, the present study focused on a new indicator called critical speed (), which is calculated by adding the speed of the vehicle going through an intersection on a major road with the time taken by the immediate approaching vehicle (TTIA) to reach the conflict point of the vehicle leaving the intersection. RTCCs were identified using critical speeds from video data collected at five unsignalized T-intersections in three cities in India. A fixed-parameter binary logistic model (FPBLM) and a random-parameter binary logistic model (RPBLM) were developed to explore the contributing factors of severe RTCCs. The RPBLM results suggest that a higher proportion of two-wheelers and cars in conflicting vehicles significantly increases the severity of RTCCs. The study findings also indicated that traffic characteristics and driver behavioral characteristics need to be controlled in order to decrease the severity of RTCCs. The findings of this study help traffic engineers and safety experts to take suitable traffic management measures to reduce RTCCs at unsignalized T-intersections.
Modeling the Severity of Right-Turn Crossing Conflicts at Unsignalized T-Intersections Using a Random-Parameter Binary Logit Model
https://orcid.org/0000-0003-4475-2573
https://orcid.org/0000-0002-0636-1355
In India, unsignalized T-intersections are more complex for the right-turn movement of vehicular traffic, which leads to serious crashes. The existing surrogate safety measures are not effective in computing the severity of right-turn crossing conflicts (RTCCs). In this context, the present study focused on a new indicator called critical speed (), which is calculated by adding the speed of the vehicle going through an intersection on a major road with the time taken by the immediate approaching vehicle (TTIA) to reach the conflict point of the vehicle leaving the intersection. RTCCs were identified using critical speeds from video data collected at five unsignalized T-intersections in three cities in India. A fixed-parameter binary logistic model (FPBLM) and a random-parameter binary logistic model (RPBLM) were developed to explore the contributing factors of severe RTCCs. The RPBLM results suggest that a higher proportion of two-wheelers and cars in conflicting vehicles significantly increases the severity of RTCCs. The study findings also indicated that traffic characteristics and driver behavioral characteristics need to be controlled in order to decrease the severity of RTCCs. The findings of this study help traffic engineers and safety experts to take suitable traffic management measures to reduce RTCCs at unsignalized T-intersections.
Modeling the Severity of Right-Turn Crossing Conflicts at Unsignalized T-Intersections Using a Random-Parameter Binary Logit Model
J. Transp. Eng., Part A: Systems
Bonela, Someswara Rao (author) / Kadali, B. Raghuram (author)
2025-03-01
Article (Journal)
Electronic Resource
English
| Springer Verlag | 2020
Lengths of Left-Turn Lanes at Unsignalized Intersections
| British Library Online Contents | 1995